Distributed forecasting and pricing system

ABSTRACT

A method, system and computer program product for distributed forecasting and pricing for a commodity supplied by a provider to a user. In one embodiment, the provider announces a pricing plan, and the provider receives a forecast of an estimated amount of the commodity from the provider that the user will use over a given period of time. The provider provides the user with an actual amount of the commodity over the given period; and the provider charges the user for the actual amount of the commodity provided to the user based on the pricing policy, the actual amount of the commodity provided to the user, and the accuracy of the forecast. In an embodiment, the forecast is received by the provider after the provider announces the pricing plan and before the given period begins. The forecast may be received from the user or from a third-party forecaster.

BACKGROUND OF THE INVENTION

This invention generally relates to pricing commodities, and morespecifically, to pricing commodities based on a distributed forecastingby the users of their expected use of the commodities.

An important problem for the producers of a commodity is to forecast thefuture demand for the commodity. An accurate forecast allows theproducer to optimize the amount of the commodity to produce, therebyminimizing the production cost or waste and maximizing revenue fromsales.

For example, a utility company produces electricity to accommodate alarge number of users. The challenge in forecasting use is that thedemand of each user is very uncertain and hard to predict. On the onehand, accurate forecasting is computationally hard, especially whenthere are millions of users or meters. On the other hand, inaccurateforecasts lead to wastes and inefficiencies, as, among other reasons,excess electricity cannot be readily or efficiently stored.

Better forecasts would allow the utilities to determine an optimumamount of power and how to produce that power, thereby minimizingproduction costs and environmental impact, while also maximizing theutility's revenue. For instance, by knowing the peak power demand,utilities can use generators that pollute less, and the utilities caneven integrate more renewable sources of power, such as wind, solar andothers, into the power production process.

The current approach taken by most utility companies is that theproducer of the power forecasts the demand, decides the amount of powerto produce, and sets the price. However, this approach has a number ofdrawbacks. These drawbacks include forecasting demand is very difficult,and inaccurate forecasts often lead to volatile prices and inefficientproduction. Further, as mentioned above, forecasting is computationallyhard, especially when there are millions of individual users. Inaddition, with current approaches, typically there is no input in theforecasting from the users of the power.

BRIEF SUMMARY

Embodiments of the invention provide a method, system and computerprogram product for distributed forecasting and pricing for a commoditysupplied by a provider to a user. In one embodiment, the methodcomprises the provider announcing a pricing plan; and the providerreceiving a forecast of an estimated amount of the commodity from theprovider that the user will use over a given period of time. Theprovider provides the user with an actual amount of the commodity overthe given period of time; and the provider charges the user for theactual amount of the commodity provided to the user by the provider overthe given period of time based on said pricing policy, said actualamount of the commodity provided to the user, and the accuracy of saidforecast.

In an embodiment, the forecast is received by the provider after theprovider announces the pricing plan.

In an embodiment, the forecast is received by the provider before thegiven period of time begins.

In one embodiment, the forecast is adjusted during said given period oftime.

In one embodiment, the forecast is received from the user.

In one embodiment, the forecast is received from a forecaster, differentfrom the user and the provider.

In an embodiment, the method further comprises repeating the providerreceiving a forecast, and the provider providing the user with an actualamount of the commodity, a multitude of times over a defined length oftime.

In an embodiment, the actual amount of the commodity provided to theuser is monitored, and the forecast is adjusted based on thismonitoring.

In an embodiment, the forecast includes an estimated upper limit and anestimated lower limit of the commodity from the provider that the userwill use over said given period of time.

In one embodiment, the given time period includes a multitude of timeintervals; and the forecast includes a mean of a multitude of partialamounts, each of said partial amounts being an amount of the commodityfrom the provider that the user will use in a respective one of saidmultitude of time intervals.

In an embodiment, the forecast includes a variance from a defined value.

In one embodiment, a measured confidence in the forecast is obtainedbased on a defined procedure, and the charge to the user for the actualamount of the commodity provided to the user by the provider over thegiven period of time is based on this measured confidence in theforecast.

In an embodiment, the user produces a quantity of the commodity, and theforecast includes a negative factor representing the quantity of thecommodity produced by the user.

In one embodiment, the provider consumes a quantity of the commodity,and the forecast includes a negative factor representing the quantity ofthe commodity consumed by the provider.

In an embodiment, the forecast is received from a mobile communicationsdevice.

In an embodiment, the invention provides a crowdsourced, demandforecasting system. In this system, a utility company announces apricing policy, each user forecasts their demand, and each user thenconsumes an amount of power from the utility company. The amount ofpower consumed by the user is different from their earlier forecast. Theutility then charges each user according to the announced pricingpolicy, the accuracy of the user's forecast, and the actual consumedamount of power. The closer each user's forecast is to their actualconsumption, the lower the price is to that user.

Embodiments of the invention reduce demand uncertainty throughincentives for each user either to forecast accurately, or to adjustdemand to meet their forecast.

Embodiments of the invention shift the forecast burden from the producerto the users, thus reducing the complexity of real-time forecasting thedemand of millions of users. Moreover, it is much easier for individualusers to forecast their own consumption. Each user can calibrate theirforecast over time by monitoring their actual consumption. Theproduction savings realized by the producer from these more accurateforecasts may be passed on to the users through the incentives.

Embodiments of the invention present a multi-step protocol between theproducer and the consumer, each submitting information to the other inalternate time steps. This protocol gives the consumer sufficientincentive to provide accurate and useful forecast of demand.

The main current pricing approaches in the utility industry includetime-of-use pricing, time-varying or dynamic pricing, hedging, andmaximum import capacity. With time-of-use pricing, the cost of the powerprovided to the users varies according to the time of the day and thedemand. With time-varying or dynamic pricing, also referred to ascritical peak pricing, the supplier publishes a price c_(t) for eachinstant of time t. The user can observe or try to forecast c_(t), andadjust their power use based on these observations or forecasts.

With the hedging pricing approach, the supplier allows consumers to payfor power ahead of time—that is, before the users actually use thepower. With the maximum import capacity pricing practice, each consumerchooses a maximum consumption level (that remains fixed for long periodsof time), and pays penalties if their actual demand exceeds that maximumconsumption level. However, consumers do not provide different forecastsof their use for different hours of the day, and do not submit real-timeforecasts.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a timing diagram of the interactions between a producer and aconsumer in embodiments of the invention.

FIG. 2 illustrates, as an example, that a user may communicate aforecast to a producer of a commodity or to a third party.

FIG. 3 shows an example of a user forecast and an actual consumption ofa commodity by the user.

FIG. 4 shows a computing environment in which embodiments of theinvention may be implemented.

DETAILED DESCRIPTION

As will be appreciated by one skilled in the art, embodiments of thepresent invention may be embodied as a system, method or computerprogram product. Accordingly, embodiments of the present invention maytake the form of an entirely hardware embodiment, an entirely softwareembodiment (including firmware, resident software, micro-code, etc.) oran embodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, embodiments of the present invention may take the form of acomputer program product embodied in any tangible medium of expressionhaving computer usable program code embodied in the medium.

Any combination of one or more computer usable or computer readablemedium(s) may be utilized. The computer-usable or computer-readablemedium may be, for example but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,device, or propagation medium. More specific examples (a non-exhaustivelist) of the computer-readable medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CDROM), an optical storage device, a transmission media such as thosesupporting the Internet or an intranet, or a magnetic storage device.Note that the computer-usable or computer-readable medium could even bepaper or another suitable medium, upon which the program is printed, asthe program can be electronically captured, via, for instance, opticalscanning of the paper or other medium, then compiled, interpreted, orotherwise processed in a suitable manner, if necessary, and then storedin a computer memory. In the context of this document, a computer-usableor computer-readable medium may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.The computer-usable medium may include a propagated data signal with thecomputer-usable program code embodied therewith, either in baseband oras part of a carrier wave. The computer usable program code may betransmitted using any appropriate medium, including but not limited towireless, wireline, optical fiber cable, RF, etc.

Computer program code for carrying out operations of the presentinvention may be written in any combination of one or more programminglanguages, including an object oriented programming language such asJava, Smalltalk, C++ or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider).

The present invention is described below with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the invention. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerprogram instructions. These computer program instructions may beprovided to a processor of a general purpose computer, special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions, which execute via the processor ofthe computer or other programmable data processing apparatus, createmeans for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks. These computer programinstructions may also be stored in a computer-readable medium that candirect a computer or other programmable data processing apparatus tofunction in a particular manner, such that the instructions stored inthe computer-readable medium produce an article of manufacture includinginstruction means which implement the function/act specified in theflowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide processes for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

The present invention relates to pricing a consumable commodity. Inembodiments of the invention, the commodity is priced in retrospectbased on actual consumption and the quality of an earlier forecast byeach user of their expected consumption. More specifically, inembodiments of the invention, with reference to FIG. 1, the producer, at102 announces a pricing policy p, which may be, for instance, a table ofprices. Each user, at 104, first submits a forecast {circumflex over(x)} of their expected use to the producer, and for example, this may bea forecast of the user's demand over the next twenty-four hours.

The producer, at 106, produces an amount of the commodity based on theforecasts of all the users. At 110, an amount x of the commodity is thenconsumed by the users or consumers. The producer, at 112, can observethis amount x through, for example, smart meters or other devices.Thereafter, each user, at 114, pays the producer an amount p(x,∥x−{circumflex over (x)}∥) that depends on the actual amount x consumedby the user, the quality of the user's forecast ∥x−{circumflex over(x)}∥, and the pricing policy p announced by the producer. Theseinteractions can be repeated in real-time.

A wide range of commodities can be used in embodiments of the invention.For example, the commodity can be electricity, water, data bandwidth, orother articles, compositions, or services. In addition, the demand canbe negative to accommodate users that are also suppliers of thecommodity, and producers that are also consumers of the commodity.

As depicted in FIG. 2, the forecasts from the users 202 can be submittedto the providers 204 through the Internet 206, including via mobiledevices 210 such as smartphones with dedicated applications. Theforecasts can be of a mean, of a variance, or of upper and lower boundsof expected use, or the forecasts may be more complex. The forecasts, inembodiments of the invention, may be generated by one or more thirdparty forecasters 212, or by one or more algorithms in place of theusers.

The pricing function p can be simple or complex. In general, the price pis increasing with increases in the forecast error. Also, pricing mayincrease with increased uncertainty in the forecast. Many measures oferror may be used in embodiments of the invention, such as a combinationof mean and variance.

FIG. 3 shows a forecast 302 and the actual consumption 304 in anotherexample of an embodiment of the invention. This forecast includes, upperand lower bounds 306 and 310 that are adjusted overtime. In thisexample, there is one supplier with many users. The supplier publishes atable of values p. Every twenty-for hours, each user submits a forecastfor its own demand for each of the next twenty-for hours. The forecastsmay be submitted, for instance, via the Internet. One embodiment of theforecast, shown in FIG. 3, includes upper bounds (U(1) . . . U(24) andlower bounds (L(1) . . . L(24), for the consumption or use of thecommodity for each of the next twenty-four hours. In this example, overperiod P, the actual consumption is less than the forecast lower bound,at period P₂, the actual consumption in greater than the forecast upperbound.

The user and the provider, for example a utility provider, observe theactual consumption f(t) from, for example, data obtained from smartmeters. The provider then charges each user a cost according to theuser's forecast, the actual consumption, and the published table p. Inthis example, p is a function, p(f,L,U), of p, L and U.

As an example, consider a consumer who forecasts his electricityconsumption for a particular week as follows:

Sun. Mon. Tue. Wed. Thur. Fri. Sat. Estimated daily 10-12 6-8 6-8 4-66-8 9-11 12-15 consumption (in watts)The actual consumption turns out to be:

Sun. Mon. Tue. Wed. Thur. Fri. Sat. (in watts) 13 5 4 5 7 10 14

The consumer thus has one day (Sunday) in which actual consumption wasmore than the forecast, and two days (Monday and Tuesday) in whichactual consumption was less than the forecast.

The utilities pricing function is to charge the user $0.12/watt foractual consumption, with adjustments based on the accuracy of theforecast. For instance, for each watt in excess of a daily forecast, theutility may charge $0.36/watt, and for each day that actual consumptionis below the forecast, the user is charged a penalty of $0.10.In this example, then, the charge to the consumer is:

-   -   ($0.12/watt)(58 watts)+($0.36/watt)(1 watt)+($0.10/day)(2 days)        =$6.96+$0.36+$0.20=$7.52

As a variation on this example, the utility might provide users with anincentive to give forecasts with narrow ranges. The utility might givethe consumer a discount of $0.01 for each daily range that is 2 watts orless, and add a penalty of $0.05 for each daily range that is 3 watts ormore.

In this variation, the charge to the consumer would become:

-   -   $7.52−($0.01)(6)+($0.05)(1)=$7.52−$0.06+$0.05=$7.51

Embodiments of the invention may be used, for example, in the powergeneration industry to eliminate the need to build additional powerplants to meet peak demand by an ICT solution that provides betterforecasts of the demand in short and long term horizons. The distributedforecasting system reduces demand uncertainty through incentives foreach user either to forecast accurately or to adjust demand to meettheir forecast.

Embodiments of the invention are advantageous to users, who are usuallyreluctant to participate in price-responsiveness pricing plans incurrent power systems. This is because it is much easier for a user toforecast their demand than it is to change their habits under dynamicpricing plans. Moreover, providing users with an opportunity to help theenvironment is itself a strong incentive to users to participate inembodiments of the invention. A user-friendly interface in a smartphoneapp is an additional incentive to users to provide accurate forecasts.

Embodiments of the invention shift the burden of demand forecast fromthe utility company to the users, thus reducing the complexity ofreal-time forecasting the demand of millions of users. Moreover, it ismuch easier for individual users to forecast their own consumption. Eachuser can calibrate their forecasts over time by monitoring their actualconsumption. The production savings realized by the producer from thesemore accurate forecasts may be passed on to the users through theincentives.

Embodiments of the invention are also advantageous to the utilitycompanies. From a cost-benefit perspective, the cost to the utilities ofimplementing the incentive system of embodiments of the invention ispotentially very low compared to the benefits resulting from theaccuracy of the forecasts from the users. The ICT infrastructure forcollecting the forecasts is the Internet, which is widely available. Inmany cases, real-time demand for individual users can already becollected from the smart meters already deployed or being deployed. Theinterface through which users submit their forecasts can beinexpensively implemented as smartphone applications.

The costs of embodiments of the invention are also offset by decreasingthe effect of, for example, power generation on the environment,compared with alternatives that require building massiveinfrastructures.

With reference to FIG. 4, an exemplary system for implementing theinvention includes a general purpose-computing device in the form of acomputer 410. Components of computer 410 may include, but are notlimited to, a processing unit 420, a system memory 430, and a system bus421 that couples various system components including the system memoryto the processing unit 420. The system bus 421 may be any of severaltypes of bus structures including a memory bus or memory controller, aperipheral bus, and a local bus using any of a variety of busarchitectures. By way of example, and not limitation, such architecturesinclude Industry Standard Architecture (ISA) bus, Micro ChannelArchitecture (MCA) bus, Enhanced ISA (EISA) bus, Video ElectronicsStandards Association (VESA) local bus, and Peripheral ComponentInterconnect (PCI) bus (also known as Mezzanine bus).

Computer 410 typically includes a variety of computer readable media.Computer readable media can be any available media that can be accessedby computer 410 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer readableinstructions, data structures, program modules or other data. Computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CDROM, digital versatile disks (DVD)or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the desired information and which canbe accessed by computer 410.

Communication media typically embodies computer readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared, and other wireless media. Combinations of any ofthe above should also be included within the scope of computer readablemedia.

The system memory 430 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 431and random access memory (RAM) 432. A basic input/output system 433(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 410, such as during start-up, istypically stored in ROM 431. RAM 432 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 420. By way of example, and notlimitation, FIG. 4 illustrates operating system 434, applicationprograms 435, other program modules 436, and program data 437.

The computer 410 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 4 illustrates a hard disk drive 441 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 451that reads from or writes to a removable, nonvolatile magnetic disk 452,and an optical disk drive 455 that reads from or writes to a removable,nonvolatile optical disk 456, such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 441 is typically connectedto the system bus 421 through a non-removable memory interface such asinterface 440, and magnetic disk drive 451 and optical disk drive 455are typically connected to the system bus 421 by a removable memoryinterface, such as interface 450.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 4 provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 410. In FIG. 4, for example, hard disk drive 441 is illustratedas storing operating system 444, application programs 445, other programmodules 446, and program data 447. Note that these components can eitherbe the same as or different from operating system 434, applicationprograms 435, other program modules 436, and program data 437. Operatingsystem 444, application programs 445, other program modules 446, andprogram data 447 are given different numbers here to illustrate that, ata minimum, they are different copies.

A user may enter commands and information into the computer 410 throughinput devices such as a keyboard 462 and pointing device 461, commonlyreferred to as a mouse, trackball or touch pad. Other input devices (notshown) may include a microphone, joystick, game pad, satellite dish,scanner, or the like. These and other input devices are often connectedto the processing unit 420 through a user input interface 460 that iscoupled to the system bus 421, but may be connected by other interfaceand bus structures, such as a parallel port, game port or a universalserial bus (USB).

A monitor 491 or other type of display device is also connected to thesystem bus 421 via an interface, such as a video interface 490. Agraphics interface 482, such as Northbridge, may also be connected tothe system bus 421. Northbridge is a chipset that communicates with theCPU, or host-processing unit 420, and assumes responsibility foraccelerated graphics port (AGP) communications. One or more graphicsprocessing units (GPUs) 484 may communicate with graphics interface 482.In this regard, GPUs 484 generally include on-chip memory storage, suchas register storage and GPUs 484 communicate with a video memory 486.GPUs 484, however, are but one example of a coprocessor and thus avariety of co-processing devices may be included in computer 410. Amonitor 491 or other type of display device is also connected to thesystem bus 421 via an interface, such as a video interface 490, whichmay in turn communicate with video memory 486. In addition to monitor491, computers may also include other peripheral output devices such asspeakers 497 and printer 496, which may be connected through an outputperipheral interface 495.

The computer 410 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer480. The remote computer 480 may be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the computer 410, although only a memory storage device 481 has beenillustrated in FIG. 4. The logical connections depicted in FIG. 4include a local area network (LAN) 471 and a wide area network (WAN)473, but may also include other networks. Such networking environmentsare commonplace in offices, enterprise-wide computer networks, intranetsand the Internet.

When used in a LAN networking environment, the computer 410 is connectedto the LAN 471 through a network interface or adapter 470. When used ina WAN networking environment, the computer 410 typically includes amodem 472 or other means for establishing communications over the WAN473, such as the Internet. The modem 472, which may be internal orexternal, may be connected to the system bus 421 via the user inputinterface 460, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 410, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 4 illustrates remoteapplication programs 485 as residing on memory device 481. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

One of ordinary skill in the art can appreciate that a computer 410 orother client device can be deployed as part of a computer network. Inthis regard, the present invention pertains to any computer systemhaving any number of memory or storage units, and any number ofapplications and processes occurring across any number of storage unitsor volumes. The present invention may apply to an environment withserver computers and client computers deployed in a network environment,having remote or local storage. The present invention may also apply toa standalone computing device, having programming languagefunctionality, interpretation and execution capabilities.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orto limit the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope of the invention. The embodiments werechosen and described in order to explain the principles and applicationof the invention, and to enable others of ordinary skill in the art tounderstand the invention. The invention may be implements in variousembodiments with various modifications as are suited to the particularuse contemplated.

What is claimed is:
 1. A method of supplying a commodity from a provider to a plurality of users, and pricing the commodity, based on collecting distributed forecasting of use of the commodity, the method comprising: providing, by the provider, a pricing plan for the commodity; providing remote access over a distributed computer network to a plurality of users via a mobile communication device application; receiving, by the provider, a forecast from each of the plurality of users, at a computer processing system, via the distributed computer network, of an estimated amount of the commodity from the provider that each of the plurality of users will use over a given period of time; determining, by the provider, at the computer processing system, an amount of the commodity to be produced based on all the forecasts received from the plurality of users over the distributed computer network; producing, by the provider, the determined amount of the commodity based on all the forecasts received from the plurality of users over the distributed computer network; providing, by the provider, each of the plurality of users with an actual amount of the commodity over the given period of time; determining, by the provider, at the computer processing system, a price for each respective user of the plurality of users for the commodity, in retrospect, after the given period of time, and charging each respective user said determined price, for the actual amount of the commodity provided to each respective user by the provider over the given period of time, based on said pricing plan, said actual amount of the commodity provided to each respective user, and an accuracy of the forecast of the estimated amount of the commodity that each respective user will use, said accuracy determined by a difference between said forecast and the actual amount of the commodity provided to each respective user by the provider; and monitoring information from a smart meter indicating the actual amount of the commodity provided to one or more respective users and adjusting the forecast based on the monitored information in determining the price for the commodity for each of the one or more respective users.
 2. The method according to claim 1, further comprising monitoring the actual amount of the commodity provided to a respective user and increasing the price for the respective user in response to the actual amount of the provided commodity being greater than the forecast.
 3. The method according to claim 2, further comprising monitoring the actual amount of the commodity provided to a respective user and decreasing the price for the respective user in response to the actual amount of the provided commodity being less than the forecast.
 4. The method according to claim 1, further comprising receiving a forecast for the estimated amount of the commodity that the plurality of users will use over the given period of time from a forecaster, different from the plurality of users and the provider.
 5. The method according to claim 1, further comprising repeating the provider receiving a forecast, and the provider providing each of the plurality of users with an actual amount of the commodity, a multitude of times over a defined length of time.
 6. The method according to claim 1, wherein the forecast from one or more of the plurality of users includes an estimated upper limit and an estimated lower limit of the commodity from the provider that the one or more of the plurality of users will use over said given period of time.
 7. The method according to claim 1, wherein: the given time period includes a multitude of time intervals; and the forecast from one or more of the plurality of users includes a mean of a multitude of partial amounts, each of said partial amounts being an amount of the commodity from the provider that the one or more of the plurality of users will use in a respective one of said multitude of time intervals.
 8. The method according to claim 7, wherein the forecast includes a variance from a defined value.
 9. The method according to claim 1, further comprising: determining uncertainty in the forecast based on forecast errors; and increasing the price based on the uncertainty.
 10. The method according to claim 1, further comprising: receiving, by the provider, information from one or more users indicating a quantity of the commodity produced by the one or more users; and adjusting the amount of the commodity to be produced based on the quantity of the commodity produced by the one or more users.
 11. The method according to claim 1, further comprising: the provider consuming a quantity of the commodity; and: adjusting the amount of the commodity to be produced based on the quantity of the commodity consumed by the provider.
 12. A computer program product, comprising: at least one tangible computer readable hardware device having computer readable program code logic tangibly embodied therein to price a commodity supplied to a user from a supplier based on collecting distributed forecasting of use of the commodity from a plurality of users, said computer readable program code, when executing in a computer, implementing the method comprising: providing a pricing policy specified by the provider for the commodity; providing remote access over a distributed computer network to a plurality of users via a mobile communication device application; receiving a forecast from each of the plurality of users, at a computer processing system, via a distributed computer network, to the provider of an estimated amount of the commodity from the provider that each of the plurality of users will use over a given period of time; determining, at the computer processing system, an amount of the commodity to be produced based on all the forecasts received from the plurality of users over the distributed computer network; determining, at the computer processing system, a price for each respective user of the plurality of users for the commodity, in retrospect, after the given period of time, and charging each respective user said determined price, for an actual amount of the commodity provided to each respective user by the provider over the given period of time, based on the pricing policy specified by the provider, the produced amount of the commodity based on all the forecasts, said actual amount of the commodity provided to each respective user, and an accuracy of the forecast of the estimated amount of the commodity that each respective user will use, said accuracy determined by a difference between said forecast and the actual amount of the commodity provided to each respective user by the supplier; and monitoring information from a smart meter indicating the actual amount of the commodity provided to one or more respective users and adjusting the forecast based on the monitored information in determining the price for the commodity for each of the one or more respective users.
 13. The computer program product according to claim 12, further comprising monitoring the actual amount of the commodity provided to a respective user and increasing the price for the respective user in response to the actual amount of the provided commodity being greater than the forecast.
 14. The computer program product according to claim 13, further comprising monitoring the actual amount of the commodity provided to a respective user and decreasing the price for the respective user in response to the actual amount of the provided commodity being less than the forecast.
 15. The computer program product according to claim 12, further comprising receiving a forecast for the an estimated amount of the commodity that the plurality of users will use over the given period of time from a forecaster, different from the plurality of users and the provider.
 16. The computer program product according to claim 12, further comprising repeating the provider receiving a forecast, and the provider providing each of the plurality of users with an actual amount of the commodity, a multitude of times over a defined length of time.
 17. The computer program product according to claim 12, wherein the forecast from one or more of the plurality of users includes an estimated upper limit and an estimated lower limit of the commodity from the provider that the one or more of the plurality of users will use over said given period of time.
 18. The computer program product according to claim 12, wherein: the given time period includes a multitude of time intervals; and the forecast from one or more of the plurality of users includes a mean of a multitude of partial amounts, each of said partial amounts being an amount of the commodity from the provider that the one or more of the plurality of users will use in a respective one of said multitude of time intervals.
 19. The computer program product according to claim 12, wherein the forecast includes a variance from a defined value.
 20. The computer program product according to claim 12, further comprising: determining uncertainty in the forecast based on forecast errors; and increasing the price based on the uncertainty.
 21. The computer program product according to claim 12, further comprising: receiving, by the provider, information from one or more users indicating a quantity of the commodity produced by the one or more users; and adjusting the amount of the commodity to be produced based on the quantity of the commodity produced by the one or more users.
 22. The computer program product according to claim 12, further comprising: the provider consuming a quantity of the commodity; and adjusting the amount of the commodity to be produced based on the quantity of the commodity consumed by the provider. 